Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations10127
Missing cells44
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 MiB
Average record size in memory482.3 B

Variable types

Numeric8
Categorical6
Text3

Alerts

isFlaggedFraud has constant value "0" Constant
Unnamed: 0 is highly overall correlated with stepHigh correlation
amount is highly overall correlated with newbalanceDest and 1 other fieldsHigh correlation
newbalanceDest is highly overall correlated with amount and 1 other fieldsHigh correlation
newbalanceOrig is highly overall correlated with oldbalanceOrgHigh correlation
oldbalanceDest is highly overall correlated with amount and 1 other fieldsHigh correlation
oldbalanceOrg is highly overall correlated with newbalanceOrigHigh correlation
step is highly overall correlated with Unnamed: 0High correlation
isFraud is highly imbalanced (94.2%) Imbalance
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
oldbalanceOrg has 2451 (24.2%) zeros Zeros
newbalanceOrig has 4123 (40.7%) zeros Zeros
oldbalanceDest has 5760 (56.9%) zeros Zeros
newbalanceDest has 5908 (58.3%) zeros Zeros

Reproduction

Analysis started2025-08-17 11:07:19.694364
Analysis finished2025-08-17 11:07:34.784893
Duration15.09 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5063
Minimum0
Maximum10126
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:34.967707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile506.3
Q12531.5
median5063
Q37594.5
95-th percentile9619.7
Maximum10126
Range10126
Interquartile range (IQR)5063

Descriptive statistics

Standard deviation2923.5574
Coefficient of variation (CV)0.57743579
Kurtosis-1.2
Mean5063
Median Absolute Deviation (MAD)2532
Skewness0
Sum51273001
Variance8547188
MonotonicityStrictly increasing
2025-08-17T16:37:35.240562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
6754 1
 
< 0.1%
6747 1
 
< 0.1%
6748 1
 
< 0.1%
6749 1
 
< 0.1%
6750 1
 
< 0.1%
6751 1
 
< 0.1%
6752 1
 
< 0.1%
6753 1
 
< 0.1%
6755 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
10126 1
< 0.1%
10125 1
< 0.1%
10124 1
< 0.1%
10123 1
< 0.1%
10122 1
< 0.1%
10121 1
< 0.1%
10120 1
< 0.1%
10119 1
< 0.1%
10118 1
< 0.1%
10117 1
< 0.1%

step
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2142787
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:35.453944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.4841392
Coefficient of variation (CV)0.58945775
Kurtosis-1.6769781
Mean4.2142787
Median Absolute Deviation (MAD)2
Skewness-0.18107797
Sum42678
Variance6.1709476
MonotonicityIncreasing
2025-08-17T16:37:35.673617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 2963
29.3%
1 2708
26.7%
6 1660
16.4%
2 1014
 
10.0%
5 665
 
6.6%
4 565
 
5.6%
3 552
 
5.5%
ValueCountFrequency (%)
1 2708
26.7%
2 1014
 
10.0%
3 552
 
5.5%
4 565
 
5.6%
5 665
 
6.6%
6 1660
16.4%
7 2963
29.3%
ValueCountFrequency (%)
7 2963
29.3%
6 1660
16.4%
5 665
 
6.6%
4 565
 
5.6%
3 552
 
5.5%
2 1014
 
10.0%
1 2708
26.7%

type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size555.5 KiB
PAYMENT
5541 
CASH_IN
1953 
CASH_OUT
1337 
TRANSFER
946 
DEBIT
 
346

Length

Max length8
Median length7
Mean length7.1571668
Min length5

Characters and Unicode

Total characters72452
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAYMENT
2nd rowPAYMENT
3rd rowTRANSFER
4th rowCASH_OUT
5th rowPAYMENT

Common Values

ValueCountFrequency (%)
PAYMENT 5541
54.7%
CASH_IN 1953
 
19.3%
CASH_OUT 1337
 
13.2%
TRANSFER 946
 
9.3%
DEBIT 346
 
3.4%
(Missing) 4
 
< 0.1%

Length

2025-08-17T16:37:35.944678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T16:37:36.140455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
payment 5541
54.7%
cash_in 1953
 
19.3%
cash_out 1337
 
13.2%
transfer 946
 
9.3%
debit 346
 
3.4%

Most occurring characters

ValueCountFrequency (%)
A 9777
13.5%
N 8440
11.6%
T 8170
11.3%
E 6833
9.4%
P 5541
7.6%
Y 5541
7.6%
M 5541
7.6%
S 4236
 
5.8%
_ 3290
 
4.5%
H 3290
 
4.5%
Other values (8) 11793
16.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 69162
95.5%
Connector Punctuation 3290
 
4.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 9777
14.1%
N 8440
12.2%
T 8170
11.8%
E 6833
9.9%
P 5541
8.0%
Y 5541
8.0%
M 5541
8.0%
S 4236
6.1%
H 3290
 
4.8%
C 3290
 
4.8%
Other values (7) 8503
12.3%
Connector Punctuation
ValueCountFrequency (%)
_ 3290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69162
95.5%
Common 3290
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 9777
14.1%
N 8440
12.2%
T 8170
11.8%
E 6833
9.9%
P 5541
8.0%
Y 5541
8.0%
M 5541
8.0%
S 4236
6.1%
H 3290
 
4.8%
C 3290
 
4.8%
Other values (7) 8503
12.3%
Common
ValueCountFrequency (%)
_ 3290
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 9777
13.5%
N 8440
11.6%
T 8170
11.3%
E 6833
9.4%
P 5541
7.6%
Y 5541
7.6%
M 5541
7.6%
S 4236
 
5.8%
_ 3290
 
4.5%
H 3290
 
4.5%
Other values (8) 11793
16.3%

branch
Text

Distinct135
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
2025-08-17T16:37:36.676808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length31
Median length20
Mean length8.6530068
Min length4

Characters and Unicode

Total characters87629
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowIndonesia
2nd rowIndia
3rd rowIndia
4th rowAustralia
5th rowAustralia
ValueCountFrequency (%)
unidos 1284
 
10.1%
estados 1283
 
10.1%
francia 735
 
5.8%
mexico 670
 
5.3%
australia 633
 
5.0%
alemania 499
 
3.9%
china 453
 
3.6%
brasil 444
 
3.5%
india 391
 
3.1%
reino 370
 
2.9%
Other values (145) 5976
46.9%
2025-08-17T16:37:37.718006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13961
15.9%
i 9904
 
11.3%
n 7070
 
8.1%
s 6486
 
7.4%
o 5810
 
6.6%
d 4617
 
5.3%
r 3710
 
4.2%
l 3624
 
4.1%
e 3588
 
4.1%
t 2877
 
3.3%
Other values (42) 25982
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72298
82.5%
Uppercase Letter 12666
 
14.5%
Space Separator 2611
 
3.0%
Open Punctuation 27
 
< 0.1%
Close Punctuation 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13961
19.3%
i 9904
13.7%
n 7070
9.8%
s 6486
9.0%
o 5810
8.0%
d 4617
 
6.4%
r 3710
 
5.1%
l 3624
 
5.0%
e 3588
 
5.0%
t 2877
 
4.0%
Other values (15) 10651
14.7%
Uppercase Letter
ValueCountFrequency (%)
E 1731
13.7%
U 1731
13.7%
A 1464
11.6%
I 1196
9.4%
C 941
7.4%
F 933
7.4%
M 813
6.4%
R 717
 
5.7%
B 687
 
5.4%
S 476
 
3.8%
Other values (14) 1977
15.6%
Space Separator
ValueCountFrequency (%)
2611
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84964
97.0%
Common 2665
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13961
16.4%
i 9904
11.7%
n 7070
 
8.3%
s 6486
 
7.6%
o 5810
 
6.8%
d 4617
 
5.4%
r 3710
 
4.4%
l 3624
 
4.3%
e 3588
 
4.2%
t 2877
 
3.4%
Other values (39) 23317
27.4%
Common
ValueCountFrequency (%)
2611
98.0%
( 27
 
1.0%
) 27
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13961
15.9%
i 9904
 
11.3%
n 7070
 
8.1%
s 6486
 
7.4%
o 5810
 
6.6%
d 4617
 
5.3%
r 3710
 
4.2%
l 3624
 
4.1%
e 3588
 
4.1%
t 2877
 
3.3%
Other values (42) 25982
29.7%

amount
Real number (ℝ)

High correlation 

Distinct10079
Distinct (%)99.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean104886.88
Minimum2.39
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:37.871870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.39
5-th percentile831.902
Q14397.38
median12798.31
Q3114381.77
95-th percentile438359.09
Maximum10000000
Range9999997.6
Interquartile range (IQR)109984.39

Descriptive statistics

Standard deviation270636.89
Coefficient of variation (CV)2.580274
Kurtosis376.30214
Mean104886.88
Median Absolute Deviation (MAD)11223.14
Skewness13.169771
Sum1.0619796 × 109
Variance7.3244326 × 1010
MonotonicityNot monotonic
2025-08-17T16:37:38.053071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25975.86 3
 
< 0.1%
354.75 2
 
< 0.1%
262434.54 2
 
< 0.1%
85354.69 2
 
< 0.1%
3643.48 2
 
< 0.1%
169941.73 2
 
< 0.1%
20128 2
 
< 0.1%
5146.88 2
 
< 0.1%
1937.5 2
 
< 0.1%
17246 2
 
< 0.1%
Other values (10069) 10104
99.8%
ValueCountFrequency (%)
2.39 1
< 0.1%
3.75 1
< 0.1%
4.36 1
< 0.1%
6.36 1
< 0.1%
6.42 1
< 0.1%
6.93 1
< 0.1%
8.73 1
< 0.1%
10.48 1
< 0.1%
13.54 1
< 0.1%
15.06 1
< 0.1%
ValueCountFrequency (%)
10000000 2
< 0.1%
3776389.09 1
< 0.1%
2943845.35 1
< 0.1%
2940764.72 1
< 0.1%
2930418.44 2
< 0.1%
2861134.92 1
< 0.1%
2837270.65 1
< 0.1%
2604219.11 1
< 0.1%
2576294.8 1
< 0.1%
2545478.01 1
< 0.1%
Distinct10121
Distinct (%)100.0%
Missing6
Missing (%)0.1%
Memory size588.2 KiB
2025-08-17T16:37:38.501582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.481672
Min length6

Characters and Unicode

Total characters106085
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10121 ?
Unique (%)100.0%

Sample

1st rowC1231006815
2nd rowC1666544295
3rd rowC1305486145
4th rowC840083671
5th rowC2048537720
ValueCountFrequency (%)
c1231006815 1
 
< 0.1%
c1912850431 1
 
< 0.1%
c207471778 1
 
< 0.1%
c712410124 1
 
< 0.1%
c1305486145 1
 
< 0.1%
c840083671 1
 
< 0.1%
c2048537720 1
 
< 0.1%
c90045638 1
 
< 0.1%
c154988899 1
 
< 0.1%
c1265012928 1
 
< 0.1%
Other values (10111) 10111
99.9%
2025-08-17T16:37:39.131168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13989
13.2%
C 10121
9.5%
2 9898
9.3%
0 9174
8.6%
6 9075
8.6%
8 9070
8.5%
7 9018
8.5%
9 8968
8.5%
5 8926
8.4%
4 8926
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95964
90.5%
Uppercase Letter 10121
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13989
14.6%
2 9898
10.3%
0 9174
9.6%
6 9075
9.5%
8 9070
9.5%
7 9018
9.4%
9 8968
9.3%
5 8926
9.3%
4 8926
9.3%
3 8920
9.3%
Uppercase Letter
ValueCountFrequency (%)
C 10121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95964
90.5%
Latin 10121
 
9.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13989
14.6%
2 9898
10.3%
0 9174
9.6%
6 9075
9.5%
8 9070
9.5%
7 9018
9.4%
9 8968
9.3%
5 8926
9.3%
4 8926
9.3%
3 8920
9.3%
Latin
ValueCountFrequency (%)
C 10121
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13989
13.2%
C 10121
9.5%
2 9898
9.3%
0 9174
8.6%
6 9075
8.6%
8 9070
8.5%
7 9018
8.5%
9 8968
8.5%
5 8926
8.4%
4 8926
8.4%

oldbalanceOrg
Real number (ℝ)

High correlation  Zeros 

Distinct7328
Distinct (%)72.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean883696.53
Minimum0
Maximum12900000
Zeros2451
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:39.405251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1129
median21363
Q3172432
95-th percentile6372330.9
Maximum12900000
Range12900000
Interquartile range (IQR)172303

Descriptive statistics

Standard deviation2124553.9
Coefficient of variation (CV)2.4041668
Kurtosis7.2910661
Mean883696.53
Median Absolute Deviation (MAD)21363
Skewness2.7797342
Sum8.9474274 × 109
Variance4.5137293 × 1012
MonotonicityNot monotonic
2025-08-17T16:37:39.718987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2451
 
24.2%
10500000 7
 
0.1%
10300000 6
 
0.1%
322 5
 
< 0.1%
337 5
 
< 0.1%
10700000 5
 
< 0.1%
122 5
 
< 0.1%
189 4
 
< 0.1%
10000000 4
 
< 0.1%
198 4
 
< 0.1%
Other values (7318) 7629
75.3%
ValueCountFrequency (%)
0 2451
24.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4.58 1
 
< 0.1%
9 1
 
< 0.1%
14.09 1
 
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
24 1
 
< 0.1%
ValueCountFrequency (%)
12900000 2
< 0.1%
12800000 1
 
< 0.1%
12700000 1
 
< 0.1%
12600000 1
 
< 0.1%
12500000 2
< 0.1%
12300000 2
< 0.1%
12200000 3
< 0.1%
12100000 2
< 0.1%
11900000 2
< 0.1%
11800000 2
< 0.1%

newbalanceOrig
Real number (ℝ)

High correlation  Zeros 

Distinct5937
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904470.34
Minimum0
Maximum13000000
Zeros4123
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:40.071438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10212.24
Q3170724.01
95-th percentile6498321.5
Maximum13000000
Range13000000
Interquartile range (IQR)170724.01

Descriptive statistics

Standard deviation2169946
Coefficient of variation (CV)2.3991345
Kurtosis7.0308777
Mean904470.34
Median Absolute Deviation (MAD)10212.24
Skewness2.7386293
Sum9.1595711 × 109
Variance4.7086658 × 1012
MonotonicityNot monotonic
2025-08-17T16:37:40.521576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4123
40.7%
10500000 7
 
0.1%
10700000 6
 
0.1%
10300000 6
 
0.1%
10200000 5
 
< 0.1%
11100000 4
 
< 0.1%
11200000 3
 
< 0.1%
10100000 3
 
< 0.1%
10000000 3
 
< 0.1%
12500000 3
 
< 0.1%
Other values (5927) 5964
58.9%
ValueCountFrequency (%)
0 4123
40.7%
4.58 1
 
< 0.1%
5.83 1
 
< 0.1%
14.09 1
 
< 0.1%
14.33 1
 
< 0.1%
21.47 1
 
< 0.1%
22.87 1
 
< 0.1%
28.72 1
 
< 0.1%
41.05 1
 
< 0.1%
53.15 1
 
< 0.1%
ValueCountFrequency (%)
13000000 1
 
< 0.1%
12900000 1
 
< 0.1%
12800000 1
 
< 0.1%
12700000 2
< 0.1%
12600000 1
 
< 0.1%
12500000 3
< 0.1%
12400000 1
 
< 0.1%
12300000 2
< 0.1%
12200000 3
< 0.1%
12100000 2
< 0.1%
Distinct6495
Distinct (%)64.2%
Missing6
Missing (%)0.1%
Memory size588.2 KiB
2025-08-17T16:37:41.290080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.475151
Min length7

Characters and Unicode

Total characters106019
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5905 ?
Unique (%)58.3%

Sample

1st rowM1979787155
2nd rowM2044282225
3rd rowC553264065
4th rowC38997010
5th rowM1230701703
ValueCountFrequency (%)
c985934102 62
 
0.6%
c1590550415 51
 
0.5%
c1286084959 50
 
0.5%
c1899073220 44
 
0.4%
c977993101 44
 
0.4%
c1782113663 43
 
0.4%
c451111351 43
 
0.4%
c2083562754 41
 
0.4%
c1360767589 40
 
0.4%
c1816757085 40
 
0.4%
Other values (6485) 9663
95.5%
2025-08-17T16:37:41.897360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14227
13.4%
5 9525
9.0%
2 9402
8.9%
8 9223
8.7%
0 9165
8.6%
7 9095
8.6%
3 9043
8.5%
4 8770
8.3%
9 8758
8.3%
6 8690
8.2%
Other values (2) 10121
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95898
90.5%
Uppercase Letter 10121
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14227
14.8%
5 9525
9.9%
2 9402
9.8%
8 9223
9.6%
0 9165
9.6%
7 9095
9.5%
3 9043
9.4%
4 8770
9.1%
9 8758
9.1%
6 8690
9.1%
Uppercase Letter
ValueCountFrequency (%)
M 5540
54.7%
C 4581
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 95898
90.5%
Latin 10121
 
9.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14227
14.8%
5 9525
9.9%
2 9402
9.8%
8 9223
9.6%
0 9165
9.6%
7 9095
9.5%
3 9043
9.4%
4 8770
9.1%
9 8758
9.1%
6 8690
9.1%
Latin
ValueCountFrequency (%)
M 5540
54.7%
C 4581
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14227
13.4%
5 9525
9.0%
2 9402
8.9%
8 9223
8.7%
0 9165
8.6%
7 9095
8.6%
3 9043
8.5%
4 8770
8.3%
9 8758
8.3%
6 8690
8.2%
Other values (2) 10121
9.5%

oldbalanceDest
Real number (ℝ)

High correlation  Zeros 

Distinct4161
Distinct (%)41.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean933539.3
Minimum0
Maximum19500000
Zeros5760
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:42.214796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3281934.51
95-th percentile6588780.3
Maximum19500000
Range19500000
Interquartile range (IQR)281934.51

Descriptive statistics

Standard deviation2677976.1
Coefficient of variation (CV)2.8686271
Kurtosis16.423177
Mean933539.3
Median Absolute Deviation (MAD)0
Skewness3.9098289
Sum9.4530189 × 109
Variance7.1715559 × 1012
MonotonicityNot monotonic
2025-08-17T16:37:42.443178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5760
56.9%
10900000 12
 
0.1%
10700000 9
 
0.1%
10300000 9
 
0.1%
13100000 8
 
0.1%
10100000 7
 
0.1%
10400000 7
 
0.1%
10800000 7
 
0.1%
10200000 7
 
0.1%
13300000 7
 
0.1%
Other values (4151) 4293
42.4%
ValueCountFrequency (%)
0 5760
56.9%
7 1
 
< 0.1%
23 1
 
< 0.1%
55 1
 
< 0.1%
65 1
 
< 0.1%
102 1
 
< 0.1%
122 1
 
< 0.1%
137 1
 
< 0.1%
144 1
 
< 0.1%
150 1
 
< 0.1%
ValueCountFrequency (%)
19500000 1
 
< 0.1%
19400000 2
< 0.1%
19300000 2
< 0.1%
19200000 1
 
< 0.1%
19100000 2
< 0.1%
18700000 3
< 0.1%
18500000 1
 
< 0.1%
18400000 1
 
< 0.1%
18300000 2
< 0.1%
18200000 2
< 0.1%

newbalanceDest
Real number (ℝ)

High correlation  Zeros 

Distinct1509
Distinct (%)14.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1109314.9
Minimum0
Maximum22600000
Zeros5908
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:42.697566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3252392.49
95-th percentile7526564.9
Maximum22600000
Range22600000
Interquartile range (IQR)252392.49

Descriptive statistics

Standard deviation3048644.5
Coefficient of variation (CV)2.7482229
Kurtosis13.632469
Mean1109314.9
Median Absolute Deviation (MAD)0
Skewness3.600117
Sum1.1231813 × 1010
Variance9.2942332 × 1012
MonotonicityNot monotonic
2025-08-17T16:37:42.870796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5908
58.3%
971418.91 32
 
0.3%
19200000 29
 
0.3%
10700000 25
 
0.2%
1254956.07 25
 
0.2%
12500000 23
 
0.2%
10200000 22
 
0.2%
1412484.09 22
 
0.2%
1178808.14 21
 
0.2%
1517262.16 19
 
0.2%
Other values (1499) 3999
39.5%
ValueCountFrequency (%)
0 5908
58.3%
96.88 3
 
< 0.1%
206.12 1
 
< 0.1%
324.52 1
 
< 0.1%
371.65 9
 
0.1%
658.85 1
 
< 0.1%
667.54 1
 
< 0.1%
787.49 1
 
< 0.1%
817.21 4
 
< 0.1%
823.49 3
 
< 0.1%
ValueCountFrequency (%)
22600000 1
 
< 0.1%
19200000 29
0.3%
18700000 4
 
< 0.1%
18400000 1
 
< 0.1%
18300000 1
 
< 0.1%
18100000 13
0.1%
18000000 5
 
< 0.1%
17800000 17
0.2%
17400000 1
 
< 0.1%
17300000 8
 
0.1%

unusuallogin
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.513183
Minimum0
Maximum20
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-08-17T16:37:43.028193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q316
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8092327
Coefficient of variation (CV)0.55256652
Kurtosis-1.2098878
Mean10.513183
Median Absolute Deviation (MAD)5
Skewness0.0069519359
Sum106467
Variance33.747184
MonotonicityNot monotonic
2025-08-17T16:37:43.220792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
18 550
 
5.4%
20 539
 
5.3%
2 526
 
5.2%
8 522
 
5.2%
19 522
 
5.2%
11 521
 
5.1%
12 515
 
5.1%
6 514
 
5.1%
9 514
 
5.1%
1 511
 
5.0%
Other values (11) 4893
48.3%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 511
5.0%
2 526
5.2%
3 508
5.0%
4 493
4.9%
5 484
4.8%
6 514
5.1%
7 491
4.8%
8 522
5.2%
9 514
5.1%
ValueCountFrequency (%)
20 539
5.3%
19 522
5.2%
18 550
5.4%
17 477
4.7%
16 502
5.0%
15 465
4.6%
14 467
4.6%
13 494
4.9%
12 515
5.1%
11 521
5.1%

isFlaggedFraud
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size494.6 KiB
0
10127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10127
100.0%

Length

2025-08-17T16:37:43.414295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T16:37:43.599707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10127
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10127
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10127
100.0%

Acct type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing10
Missing (%)0.1%
Memory size553.9 KiB
Savings
6987 
Current
3130 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70819
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCurrent
2nd rowSavings
3rd rowCurrent
4th rowCurrent
5th rowCurrent

Common Values

ValueCountFrequency (%)
Savings 6987
69.0%
Current 3130
30.9%
(Missing) 10
 
0.1%

Length

2025-08-17T16:37:43.751742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T16:37:43.869033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
savings 6987
69.1%
current 3130
30.9%

Most occurring characters

ValueCountFrequency (%)
n 10117
14.3%
S 6987
9.9%
a 6987
9.9%
v 6987
9.9%
i 6987
9.9%
g 6987
9.9%
s 6987
9.9%
r 6260
8.8%
C 3130
 
4.4%
u 3130
 
4.4%
Other values (2) 6260
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60702
85.7%
Uppercase Letter 10117
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10117
16.7%
a 6987
11.5%
v 6987
11.5%
i 6987
11.5%
g 6987
11.5%
s 6987
11.5%
r 6260
10.3%
u 3130
 
5.2%
e 3130
 
5.2%
t 3130
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S 6987
69.1%
C 3130
30.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 70819
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10117
14.3%
S 6987
9.9%
a 6987
9.9%
v 6987
9.9%
i 6987
9.9%
g 6987
9.9%
s 6987
9.9%
r 6260
8.8%
C 3130
 
4.4%
u 3130
 
4.4%
Other values (2) 6260
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10117
14.3%
S 6987
9.9%
a 6987
9.9%
v 6987
9.9%
i 6987
9.9%
g 6987
9.9%
s 6987
9.9%
r 6260
8.8%
C 3130
 
4.4%
u 3130
 
4.4%
Other values (2) 6260
8.8%
Distinct14
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Memory size568.8 KiB
6/1/2018
1446 
5/1/2018
724 
7/1/2018
723 
8/1/2018
723 
10/1/2018
723 
Other values (9)
5781 

Length

Max length9
Median length8.5
Mean length8.5
Min length8

Characters and Unicode

Total characters86020
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3/1/2018
2nd row5/1/2018
3rd row7/1/2018
4th row6/1/2018
5th row6/1/2018

Common Values

ValueCountFrequency (%)
6/1/2018 1446
14.3%
5/1/2018 724
 
7.1%
7/1/2018 723
 
7.1%
8/1/2018 723
 
7.1%
10/1/2018 723
 
7.1%
11/1/2018 723
 
7.1%
12/1/2018 723
 
7.1%
13/1/2018 723
 
7.1%
19/1/2018 723
 
7.1%
25/1/2018 723
 
7.1%
Other values (4) 2166
21.4%

Length

2025-08-17T16:37:44.051627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6/1/2018 1446
14.3%
5/1/2018 724
 
7.2%
7/1/2018 723
 
7.1%
8/1/2018 723
 
7.1%
10/1/2018 723
 
7.1%
11/1/2018 723
 
7.1%
12/1/2018 723
 
7.1%
13/1/2018 723
 
7.1%
19/1/2018 723
 
7.1%
25/1/2018 723
 
7.1%
Other values (4) 2166
21.4%

Most occurring characters

ValueCountFrequency (%)
1 25300
29.4%
/ 20240
23.5%
2 11567
13.4%
0 10843
12.6%
8 10843
12.6%
6 2168
 
2.5%
5 1447
 
1.7%
3 1445
 
1.7%
9 1444
 
1.7%
7 723
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65780
76.5%
Other Punctuation 20240
 
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25300
38.5%
2 11567
17.6%
0 10843
16.5%
8 10843
16.5%
6 2168
 
3.3%
5 1447
 
2.2%
3 1445
 
2.2%
9 1444
 
2.2%
7 723
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/ 20240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25300
29.4%
/ 20240
23.5%
2 11567
13.4%
0 10843
12.6%
8 10843
12.6%
6 2168
 
2.5%
5 1447
 
1.7%
3 1445
 
1.7%
9 1444
 
1.7%
7 723
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25300
29.4%
/ 20240
23.5%
2 11567
13.4%
0 10843
12.6%
8 10843
12.6%
6 2168
 
2.5%
5 1447
 
1.7%
3 1445
 
1.7%
9 1444
 
1.7%
7 723
 
0.8%

Time of day
Categorical

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size554.6 KiB
Afternoon
3627 
Night
3294 
Morning
3204 

Length

Max length9
Median length7
Mean length7.0657778
Min length5

Characters and Unicode

Total characters71541
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowMorning
3rd rowMorning
4th rowAfternoon
5th rowMorning

Common Values

ValueCountFrequency (%)
Afternoon 3627
35.8%
Night 3294
32.5%
Morning 3204
31.6%
(Missing) 2
 
< 0.1%

Length

2025-08-17T16:37:44.229629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T16:37:44.437247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon 3627
35.8%
night 3294
32.5%
morning 3204
31.6%

Most occurring characters

ValueCountFrequency (%)
n 13662
19.1%
o 10458
14.6%
t 6921
9.7%
r 6831
9.5%
i 6498
9.1%
g 6498
9.1%
A 3627
 
5.1%
f 3627
 
5.1%
e 3627
 
5.1%
N 3294
 
4.6%
Other values (2) 6498
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61416
85.8%
Uppercase Letter 10125
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 13662
22.2%
o 10458
17.0%
t 6921
11.3%
r 6831
11.1%
i 6498
10.6%
g 6498
10.6%
f 3627
 
5.9%
e 3627
 
5.9%
h 3294
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 3627
35.8%
N 3294
32.5%
M 3204
31.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 71541
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 13662
19.1%
o 10458
14.6%
t 6921
9.7%
r 6831
9.5%
i 6498
9.1%
g 6498
9.1%
A 3627
 
5.1%
f 3627
 
5.1%
e 3627
 
5.1%
N 3294
 
4.6%
Other values (2) 6498
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 13662
19.1%
o 10458
14.6%
t 6921
9.7%
r 6831
9.5%
i 6498
9.1%
g 6498
9.1%
A 3627
 
5.1%
f 3627
 
5.1%
e 3627
 
5.1%
N 3294
 
4.6%
Other values (2) 6498
9.1%

isFraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size514.4 KiB
0.0
10057 
1.0
 
68

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30375
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10057
99.3%
1.0 68
 
0.7%
(Missing) 2
 
< 0.1%

Length

2025-08-17T16:37:44.698630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T16:37:44.901578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10057
99.3%
1.0 68
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 20182
66.4%
. 10125
33.3%
1 68
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20250
66.7%
Other Punctuation 10125
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20182
99.7%
1 68
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 10125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30375
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20182
66.4%
. 10125
33.3%
1 68
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20182
66.4%
. 10125
33.3%
1 68
 
0.2%

Interactions

2025-08-17T16:37:31.953315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:21.279636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:22.827893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:24.080574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:25.369640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:27.260979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:28.470485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:30.356417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:32.110090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:21.549375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:22.983916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:24.241504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:25.481705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:27.456192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:28.684324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:30.525501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:32.260756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:21.761865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:23.166636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:24.348278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:25.682412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:27.648639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:28.961549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:30.694211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:32.459753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:21.888675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:23.437526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:24.536598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:25.928824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:27.821626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:29.211884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:31.031344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:32.653436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:22.005860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:23.547447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:24.667812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:26.153809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:27.960403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:29.545390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:31.360532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:32.819362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:22.139531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:23.671037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:24.897238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:26.453097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:28.076402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:29.713030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:31.494042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:33.062134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:22.420167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:23.792345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:25.126375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:26.736246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:28.199497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:29.864266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:31.615741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:33.227618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:22.631189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:23.911670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:25.247638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:27.018878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:28.313399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:30.103920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-17T16:37:31.747652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-08-17T16:37:45.075382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Acct typeDate of transactionTime of dayUnnamed: 0amountisFraudnewbalanceDestnewbalanceOrigoldbalanceDestoldbalanceOrgsteptypeunusuallogin
Acct type1.0000.3090.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.014
Date of transaction0.3091.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.000
Time of day0.0110.0001.0000.0000.0000.0000.0000.0150.0000.0160.0000.0120.014
Unnamed: 00.0000.0000.0001.0000.0590.054-0.005-0.074-0.040-0.0530.9750.125-0.004
amount0.0000.0000.0000.0591.0000.1800.7360.0160.7390.1070.0680.170-0.014
isFraud0.0000.0150.0000.0540.1801.0000.0000.0160.0000.0250.0560.1530.108
newbalanceDest0.0000.0000.000-0.0050.7360.0001.0000.0910.9280.1350.0000.215-0.010
newbalanceOrig0.0000.0000.015-0.0740.0160.0160.0911.0000.1800.915-0.0780.4310.020
oldbalanceDest0.0000.0000.000-0.0400.7390.0000.9280.1801.0000.183-0.0320.207-0.005
oldbalanceOrg0.0000.0000.016-0.0530.1070.0250.1350.9150.1831.000-0.0600.4230.019
step0.0000.0000.0000.9750.0680.0560.000-0.078-0.032-0.0601.0000.077-0.008
type0.0000.0000.0120.1250.1700.1530.2150.4310.2070.4230.0771.0000.019
unusuallogin0.0140.0000.014-0.004-0.0140.108-0.0100.020-0.0050.019-0.0080.0191.000

Missing values

2025-08-17T16:37:33.516725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-17T16:37:34.107365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-17T16:37:34.528340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0steptypebranchamountnameOrigoldbalanceOrgnewbalanceOrignameDestoldbalanceDestnewbalanceDestunusualloginisFlaggedFraudAcct typeDate of transactionTime of dayisFraud
001PAYMENTIndonesia9839.64C1231006815170136.00160296.36M19797871550.00.0090Current3/1/2018Morning0.0
111PAYMENTIndia1864.28C166654429521249.0019384.72M20442822250.00.00100Savings5/1/2018Morning0.0
221TRANSFERIndia181.00C1305486145181.000.00C5532640650.00.0020Current7/1/2018Morning1.0
331CASH_OUTAustralia181.00C840083671181.000.00C3899701021182.00.0010Current6/1/2018Afternoon1.0
441PAYMENTAustralia11668.14C204853772041554.0029885.86M12307017030.00.00170Current6/1/2018Morning0.0
551PAYMENTAustralia7817.71C9004563853860.0046042.29M5734872740.00.00140Current8/1/2018Morning0.0
661PAYMENTChina7107.77C154988899183195.00176087.23M4080691190.00.00160NaN9/1/2018Night0.0
771PAYMENTChina7861.64C1912850431176087.23168225.59M6333263330.00.00190Current10/1/2018Morning0.0
881PAYMENTChina4024.36C12650129282671.000.00M11769321040.00.00100Current11/1/2018Morning0.0
991DEBITChina5337.77C712410124NaN36382.23C19560086041898.040348.7990Current12/1/2018Night0.0
Unnamed: 0steptypebranchamountnameOrigoldbalanceOrgnewbalanceOrignameDestoldbalanceDestnewbalanceDestunusualloginisFlaggedFraudAcct typeDate of transactionTime of dayisFraud
10117101177PAYMENTMexico8315.88C172800204787316.0779000.19M10025444450.00.090Savings12/1/2018Night0.0
10118101187PAYMENTMexico1507.32C180706499622377.0020869.68M14396210820.00.070Current13/1/2018Morning0.0
10119101197CASH_OUTRepublica Dominicana3387.85C124264712320869.6817481.83C45143383616148.00.0140Savings16/1/2018Morning0.0
10120101207PAYMENTMexico2711.69C210646588111278.008566.31M9732072830.00.040Savings19/1/2018Night0.0
10121101217CASH_OUTEl Salvador34311.33C4290514948566.310.00C578294406272678.01909674.320Savings25/1/2018Night0.0
10122101227PAYMENTCuba337.50C149430600533107.0032769.50M14240270000.00.070Current3/1/2018Afternoon0.0
10123101237PAYMENTMexico5003.57C163389016932769.5027765.93M18547458050.00.0110Savings5/1/2018Morning0.0
10124101247PAYMENTPanama10424.89C102613866950780.0040355.11M18529003170.00.060Savings7/1/2018Night0.0
10125101257PAYMENTMexico2823.59C378659213986.000.00M3018129500.00.0120Savings6/1/2018Night0.0
10126101267PAYMENTCuba8126.71C16392960146423.000.00M1297746060.00.0110Current2/1/2018Afternoon0.0